In response to this problem, inspired by the co-training algorithm[1]} and ensemble learning[2]}, we propose an Ensemble Co-training algorithm (ECT) for ZSL. The algorithm uses the difference information between different views of data or different classifiers to learn from each other, and combines unlabeled samples to optimize the performance of classifiers or related network models. Literature[3]}, [4]} has proved that for the same dataset, the models learned from different network structures have different prediction distributions for the test set. The basic idea of the ECT we proposed is (1) Construct the view features of different network structures of the image to replace the different views required in the co-training algorithm, and ensemble multiple different classifiers to adaptively reduce the prediction error in the image label embedding from multiple angles. (2) In the training phase, reliable unseen data is added to alleviate the domain shift problem.
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